Reimagining user feedback for the modern audience with micro-surveys

Hacker News

Hey HN, For a few years I've been experimenting with a new way of gather user feedback that doesn't hinge on invasive privacy practices. While technology has moved forward at unimaginable speed, current approaches to user feedback surveys are simply digital clipboard and pen and I'd like to change that. I launched https://www.onva.io/ recently and would appreciate any questions or feedback that people have! I wanted to build a platform which: - DOESN'T invade user's privacy - DOESN'T insist users go to another platform to provide feedback - DOESN'T ask something from the user without providing anything back - DOESN'T ask people pointless questions - DOESN'T waste people's time with long surveys So I created a platform which: - delivers micro surveys, so not to waste a user's time with many questions at once - integrates seamlessly with existing products without pushing users elsewhere - offers an incentive structure to value user's time driving soaring engagement - targets appropriate questions to users using dynamic targetting rules - helps to understand the group over time, rather than an individual once Beta is now open and free, and I'd love to answer any questions you might have! Thanks

Visit product View on Hacker NewsMar 31, 2021

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Replace email lists and surveys with a modern user feedback forum40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Replace email lists and surveys with a modern user feedback forum

Hacker News1

Blatta – User Feedback in Seconds38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Blatta – User Feedback in Seconds

Hacker News8

The #1 Reason Your Product Fails – Ignoring User Feedback38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The #1 Reason Your Product Fails – Ignoring User Feedback

Hacker News2

FeedbackPoster65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

User feedback software

Indie Hackers1analytics

Automate User Feedback36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automate User Feedback

Hacker News1

Ask Users82%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

User feedback platform with surveys and widgets

Indie Hackers1design

UserInsights – Get 5 minutes of real user feedback39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

UserInsights – Get 5 minutes of real user feedback

Hacker News31

A way to respect all your user feedback and your time44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A way to respect all your user feedback and your time

Hacker News3

Usersnap – Collect User Feedback40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Usersnap – Collect User Feedback

Hacker News1

askghost33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get real user feedback with one email.

Indie Hackerscommitment-side-project

Browse all products Analyze your launch